MR Blood Oxygenation Level–Dependent Signal Differences in Parenchymal and Large Draining Vessels: Implications for Functional MR Imaging

Abstract

BACKGROUND AND PURPOSE: One major limitation of current functional MR (fMR) imaging is its inability to clarify the relationship between sites of cortical neuronal activation, small parenchymal venules that are in close proximity to these sites, and large draining veins distant from the active parenchyma. We propose to use gradient-echo blood oxygenation level–dependent (BOLD) fMR time courses to differentiate large draining veins from parenchymal microvasculature.

METHODS: In eight research subjects, five of whom presented with space-occupying lesions near the central sulcus, gradient-echo fMR imaging was performed during alternating periods of rest and motor activation. MR signal time courses from parenchymal regions and draining veins of different diameters, which were identified using contrast-enhanced T1-weighted scans, were evaluated. Percent signal changes (ΔS) and the time to the onset of MR signal rise (T₀) were calculated.

RESULTS: Mean ΔS for all subjects was 2.3% (SD ±0.7%) for parenchymal activation, 4.3% (SD ±1.0%) for sulcal macrovasculature, and 7.3 (SD ±1.1%) for large superficial bridging veins. The mean time to onset of MR signal increase was 4.4 seconds for parenchymal task-related hemodynamic changes and 6.6 seconds for venous hemodynamic changes, regardless of vessel size. Both the differences in ΔS and T₀ were statistically significant between venous and parenchymal activation (P < .0001).

CONCLUSION: Gradient-echo fMR imaging reveals hemodynamic task-related changes regardless of vessel size and therefore might show macrovascular changes distal to the site of neuronal activity. MR-signal time-course characteristics (ΔS and T₀) can be used to differentiate between small parenchymal and larger pial draining vessels, which is especially important in presurgical planning of neurosurgical procedures involving functionally important brain regions. The knowledge about the differences in ΔS and T₀ between micro- and macrovasculature might lead to a more accurate description of the spatial distribution of underlying neuronal activity.